Time series forecasting methodology for multiple-step-ahead prediction

Mathematics and Statistics

Keywords

Clustering, Computational intelligence, Forecasting, Neu-ral networks

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Time series forecasting methodology for multiple-step-ahead prediction. / Pavlidis, N. G.; Tasoulis, D. K.; Vrahatis, M. N.
Proceedings of the IASTED International Conference on Computational Intelligence. 2005. p. 456-461 (Proceedings of the IASTED International Conference on Computational Intelligence; Vol. 2005).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Pavlidis, NG, Tasoulis, DK & Vrahatis, MN 2005, Time series forecasting methodology for multiple-step-ahead prediction. in Proceedings of the IASTED International Conference on Computational Intelligence. Proceedings of the IASTED International Conference on Computational Intelligence, vol. 2005, pp. 456-461, IASTED International Conference on Computational Intelligence, Calgary, AB, Canada, 4/07/05.

APA

Pavlidis, N. G., Tasoulis, D. K., & Vrahatis, M. N. (2005). Time series forecasting methodology for multiple-step-ahead prediction. In Proceedings of the IASTED International Conference on Computational Intelligence (pp. 456-461). (Proceedings of the IASTED International Conference on Computational Intelligence; Vol. 2005).

Vancouver

Pavlidis NG, Tasoulis DK, Vrahatis MN. Time series forecasting methodology for multiple-step-ahead prediction. In Proceedings of the IASTED International Conference on Computational Intelligence. 2005. p. 456-461. (Proceedings of the IASTED International Conference on Computational Intelligence).

Author

Pavlidis, N. G. ; Tasoulis, D. K. ; Vrahatis, M. N. / Time series forecasting methodology for multiple-step-ahead prediction. Proceedings of the IASTED International Conference on Computational Intelligence. 2005. pp. 456-461 (Proceedings of the IASTED International Conference on Computational Intelligence).

Bibtex

@inproceedings{8872e768234248dcab570fa13e83b296,

title = "Time series forecasting methodology for multiple-step-ahead prediction",

abstract = "This paper presents a time series forecasting methodology and applies it to generate multiple-step-ahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time series analysis, clustering, and artificial neural networks. In brief, clustering is applied to identify neighborhoods in the reconstructed state space of the system; and subsequently neural networks are trained to model the dynamics of each neighborhood separately. The results obtained through this approach are promising.",

keywords = "Clustering, Computational intelligence, Forecasting, Neu-ral networks",

author = "Pavlidis, {N. G.} and Tasoulis, {D. K.} and Vrahatis, {M. N.}",

year = "2005",

month = dec,

day = "1",

language = "English",

isbn = "0889864810",

series = "Proceedings of the IASTED International Conference on Computational Intelligence",

pages = "456--461",

booktitle = "Proceedings of the IASTED International Conference on Computational Intelligence",

note = "IASTED International Conference on Computational Intelligence ; Conference date: 04-07-2005 Through 06-07-2005",

}

RIS

TY - GEN

T1 - Time series forecasting methodology for multiple-step-ahead prediction

AU - Pavlidis, N. G.

AU - Tasoulis, D. K.

AU - Vrahatis, M. N.

PY - 2005/12/1

Y1 - 2005/12/1

N2 - This paper presents a time series forecasting methodology and applies it to generate multiple-step-ahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time series analysis, clustering, and artificial neural networks. In brief, clustering is applied to identify neighborhoods in the reconstructed state space of the system; and subsequently neural networks are trained to model the dynamics of each neighborhood separately. The results obtained through this approach are promising.

AB - This paper presents a time series forecasting methodology and applies it to generate multiple-step-ahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time series analysis, clustering, and artificial neural networks. In brief, clustering is applied to identify neighborhoods in the reconstructed state space of the system; and subsequently neural networks are trained to model the dynamics of each neighborhood separately. The results obtained through this approach are promising.

KW - Clustering

KW - Computational intelligence

KW - Forecasting

KW - Neu-ral networks

M3 - Conference contribution/Paper

AN - SCOPUS:33748563066

SN - 0889864810

SN - 9780889864818

T3 - Proceedings of the IASTED International Conference on Computational Intelligence

SP - 456

EP - 461

BT - Proceedings of the IASTED International Conference on Computational Intelligence

T2 - IASTED International Conference on Computational Intelligence

Y2 - 4 July 2005 through 6 July 2005

ER -

Research

Keywords